initialized the database and edited the camera to save to the database

ServiceAccountKey.json

@@ -0,0 +1,13 @@
+{
+  "type": "service_account",
+  "project_id": "only-motion-detector",
+  "private_key_id": "32ab7946c1ef340abe95c885676fffe99354f047",
+  "private_key": "-----BEGIN PRIVATE KEY-----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\nPCqmPQOzT4odBB2ShLoIJlM=\n-----END PRIVATE KEY-----\n",
+  "client_email": "firebase-adminsdk-7cnbe@only-motion-detector.iam.gserviceaccount.com",
+  "client_id": "107965334749798166149",
+  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+  "token_uri": "https://oauth2.googleapis.com/token",
+  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/firebase-adminsdk-7cnbe%40only-motion-detector.iam.gserviceaccount.com",
+  "universe_domain": "googleapis.com"
+}

camera.py

@@ -3,19 +3,30 @@
 import paho.mqtt.client as mqtt
 import os
 from datetime import datetime
+import firebase_admin
+from firebase_admin import credentials, storage
 
 # MQTT settings
 MQTT_BROKER = "broker.hivemq.com"
 MQTT_PORT = 1883
 MQTT_TOPIC = "home/camera_motion"
 
+# Firebase settings
+FIREBASE_CREDENTIALS_PATH = "ServiceAccountKey.json"
+FIREBASE_STORAGE_BUCKET = "only-motion-detector.appspot.com"
+
+# Initialize Firebase Admin SDK
+cred = credentials.Certificate(FIREBASE_CREDENTIALS_PATH)
+firebase_admin.initialize_app(cred, {'storageBucket': FIREBASE_STORAGE_BUCKET})
+bucket = storage.bucket()
+
 # Initialize MQTT client
 client = mqtt.Client()
 client.connect(MQTT_BROKER, MQTT_PORT)
 
-# Initialize the camera 
+# Initialize the camera
 macbook_camera = cv2.VideoCapture(0)
-iphone_camera = cv2.VideoCapture(1) 
+iphone_camera = cv2.VideoCapture(1)
 
 # Get the video frame width and height for camera
 macbook_camera.set(cv2.CAP_PROP_FRAME_WIDTH, 740)
@@ -24,7 +35,6 @@
 iphone_camera.set(cv2.CAP_PROP_FRAME_WIDTH, 740)
 iphone_camera.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
 
-
 frame_width_macbook = int(macbook_camera.get(cv2.CAP_PROP_FRAME_WIDTH))
 frame_height_macbook = int(macbook_camera.get(cv2.CAP_PROP_FRAME_HEIGHT))
 
@@ -58,3 +68,92 @@
 # Read the first frame from iPhone camera (if available)
 ret_iphone, frame1_iphone = iphone_camera.read()
 ret_iphone, frame2_iphone = iphone_camera.read()
+
+while True:
+    # Process MacBook camera frames
+    if ret_macbook:
+        # Compute the absolute difference between the two frames
+        diff_macbook = cv2.absdiff(frame1_macbook, frame2_macbook)
+        gray_macbook = cv2.cvtColor(diff_macbook, cv2.COLOR_BGR2GRAY)
+        blur_macbook = cv2.GaussianBlur(gray_macbook, (5, 5), 0)
+        _, thresh_macbook = cv2.threshold(blur_macbook, 20, 255, cv2.THRESH_BINARY)
+        dilated_macbook = cv2.dilate(thresh_macbook, None, iterations=3)
+        contours_macbook, _ = cv2.findContours(dilated_macbook, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+
+        motion_detected_macbook = False
+        for contour in contours_macbook:
+            if cv2.contourArea(contour) < 500:
+                continue
+            (x, y, w, h) = cv2.boundingRect(contour)
+            cv2.rectangle(frame1_macbook, (x, y), (x + w, y + h), (0, 255, 0), 2)
+            motion_detected_macbook = True
+
+        # If motion is detected, publish to MQTT and print message
+        if motion_detected_macbook:
+            client.publish(MQTT_TOPIC, "Motion Detected (MacBook)")
+            print("Motion Detected (MacBook)")
+
+        # Write the frame to the output file for MacBook camera
+        out_macbook.write(frame1_macbook)
+
+        # Show the frame with contours for MacBook camera
+        cv2.imshow("Feed (MacBook)", frame1_macbook)
+
+        # Update the frames for MacBook camera
+        frame1_macbook = frame2_macbook
+        ret_macbook, frame2_macbook = macbook_camera.read()
+
+    # Process iPhone camera frames (if available)
+    if ret_iphone:
+        # Compute the absolute difference between the two frames (example)
+        diff_iphone = cv2.absdiff(frame1_iphone, frame2_iphone)
+        gray_iphone = cv2.cvtColor(diff_iphone, cv2.COLOR_BGR2GRAY)
+        blur_iphone = cv2.GaussianBlur(gray_iphone, (5, 5), 0)
+        _, thresh_iphone = cv2.threshold(blur_iphone, 20, 255, cv2.THRESH_BINARY)
+        dilated_iphone = cv2.dilate(thresh_iphone, None, iterations=3)
+        contours_iphone, _ = cv2.findContours(dilated_iphone, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+
+        motion_detected_iphone = False
+        for contour in contours_iphone:
+            if cv2.contourArea(contour) < 500:
+                continue
+            (x, y, w, h) = cv2.boundingRect(contour)
+            cv2.rectangle(frame1_iphone, (x, y), (x + w, y + h), (0, 255, 0), 2)
+            motion_detected_iphone = True
+
+        # If motion is detected, publish to MQTT and print message
+        if motion_detected_iphone:
+            client.publish(MQTT_TOPIC, "Motion Detected (iPhone)")
+            print("Motion Detected (iPhone)")
+
+        # Write the frame to the output file for iPhone camera (if available)
+        if iphone_camera.isOpened():
+            out_iphone.write(frame1_iphone)
+
+            # Show the frame with contours for iPhone camera (if available)
+            cv2.imshow("Feed (iPhone)", frame1_iphone)
+
+            # Update the frames for iPhone camera (if available)
+            frame1_iphone = frame2_iphone
+            ret_iphone, frame2_iphone = iphone_camera.read()
+
+    # Exit on 'q' key press
+    if cv2.waitKey(10) & 0xFF == ord('q'):
+        break
+
+# Upload to Firebase Cloud Storage
+def upload_to_firebase(file_path, blob_name):
+    blob = bucket.blob(blob_name)
+    blob.upload_from_filename(file_path)
+    print(f'File {file_path} uploaded to {blob_name}.')
+
+upload_to_firebase(mp4_video_path_macbook, f'motion_detected_macbook_{timestamp}.mp4')
+upload_to_firebase(mp4_video_path_iphone, f'motion_detected_iphone_{timestamp}.mp4')
+
+# Clean up
+macbook_camera.release()
+out_macbook.release()
+if iphone_camera.isOpened():
+    iphone_camera.release()
+    out_iphone.release()
+cv2.destroyAllWindows()